Determination of rock types by spectral scanning

ABSTRACT

Described herein is a method and system for classifying rock types in a rock body. The method comprises the steps of obtaining spectral data from a spectral measurement ( 202 ) of a surface region of the rock body and then determining a first spectral ratio between two wavelength bands of the spectral data. From the first spectral ratio it can be assessed ( 204 ) whether the measurement is a high-angle measurement, and if the measurement is not a high-angle measurement then a further spectral ratio between two further wavelength bands of the spectral data is determined ( 208 ). The further spectral ratio is then compared ( 210 ) with a corresponding diagnostic criterion to assess whether the surface region comprises a first rock type associated with the further spectral ratio and diagnostic criterion.

FIELD OF THE INVENTION

This invention relates to methods and apparatus for the determination ofrock types occurring in rock bodies. Such determinations may be madeduring ore body exploration and surveys or during mining operations in amine for ore grade assessment.

BACKGROUND OF THE INVENTION

In conventional pit-mining, ore grade assessments are made on the basisof sample material taken from spot locations. Analysis of such materialcan take many days which can delay the planning of blasting recovery andtransport of the ore material. With developments in automated miningtechniques there is a need for an improved method for accuratelyidentifying mine geology.

Reference to any prior art in the specification is not, and should notbe taken as, an acknowledgment or any form of suggestion that this priorart forms part of the common general knowledge in Australia or any otherjurisdiction or that this prior art could reasonably be expected to beascertained, understood and regarded as relevant by a person skilled inthe art.

SUMMARY OF THE INVENTION

According to one aspect, the invention provides a method of identifyingrock types in a rock body, comprising scanning a surface of the rockbody with a spectral sensor to obtain spectral data from the rock bodysurface, said spectral data comprising multiple spectra obtained fromsuccessively scanned regions of the rock body surface; determining fordifferent spectra a spectral parameter indicative of spectra derivedfrom high angle reflectance; and using data from the spectra sodetermined not to be derived from high angle reflectance to determinerock types present at said regions of the rock body surface.

Said parameter may be a ratio derived from a comparison of the spectraldata at differing specific wavelength bands.

The rock types may be determined from the spectra determined not to befrom high angle reflectance by determining for each of said spectra aplurality of further spectral ratios each derived from a comparison ofthe spectral data at differing specific wavelength bands as indicatorsof differing rock types.

The spectral ratios indicative of rock types may be determined fromspectral data at wavelength bands which are not affected by atmosphericabsorptions under natural light illumination.

The invention also extends to a method of identifying rock types in arock body, comprising scanning a surface of a rock body with a spectralsensor to obtain spectral data from the rock body surface, said datacomprising multiple spectra obtained from successively scanned regionsof the rock body surface; and determining for differing spectra aplurality of spectral ratios each determined from a comparison of thespectral data at differing specific wavelength bands as indicators ofdiffering rock types.

The rock body may be in a mine and the method may be used to make oregrade assessments of the rock body for mining.

The invention further extends to a method of mining comprising scanninga mine bench face with a spectral sensor to obtain spectral data from arock body at the bench face, said data comprising multiple spectraobtained from successively scanned regions of the rock body surface;determining for different spectra a spectral parameter indicative ofspectra derived from high angle reflectance; using data from the spectraso determined not to be derived from high angle reflectance to determinerock types at said regions of the rock body surface; removing materialfrom the bench; and transporting removed material for processing inaccordance with the rock type determination derived from the bench face.

The invention also extends to a method of mining comprising scanning amine bench face with a spectral sensor to obtain spectral data from arock body at the bench face, said data comprising multiple spectraobtained from successively scanned regions of the rock body surface;determining for different spectra a plurality of spectral ratios eachderived from a comparison of spectral data at differing specificwavelength bands as indicators of differing rock types; removingmaterial from the bench; and transporting removed material forprocessing in accordance with the rock type determinations.

Said spectra may include spectra in the Visible Near Infrared Range(VNIR) and/or in the Short Wave Infrared Range (SWIR).

The spectral data may comprise reflectance values at differingwavelengths through the spectra.

According to a further aspect of the invention there is provided amethod for classifying rock types in a rock body comprising:

obtaining spectral data from a spectral measurement of a surface regionof the rock body;

determining a first spectral ratio between two wavelength bands of thespectral data;

assessing from the first spectral ratio whether the measurement is ahigh-angle measurement;

if the measurement is not a high-angle measurement, determining afurther spectral ratio between two further wavelength bands of thespectral data; and

comparing the further spectral ratio with a corresponding diagnosticcriterion to assess whether the surface region comprises a first rocktype associated with the further spectral ratio and diagnosticcriterion.

Further aspects of the present invention and further embodiments of theaspects described in the preceding paragraphs will become apparent fromthe following description, given by way of example and with reference tothe accompanying drawings. In order that the invention may be more fullyexplained, results are also provided of its application to determinationof rock types in a project to determine how spectral data can be used toaccurately identify the rock types of the West Angelas mine, located inthe Eastern Pilbara region of Northern Western Australia.

As used herein, except where the context requires otherwise, the term“comprise” and variations of the term, such as “comprising”, “comprises”and “comprised”, are not intended to exclude further additives,components, integers or steps.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic of a system for determination of rock types byspectral scanning;

FIG. 2A is a block diagram showing method steps for determiningclassification criteria;

FIG. 2B is a block diagram showing method steps for classifying rocktypes;

FIG. 3 is a graph of the spectra of a group of material present in anore body, and the manganiferous shale ratio used to distinguishmanganiferous shale from the other material;

FIG. 4 is a graph illustrating how a manganiferous shale spectral ratiocan be used to distinguish rock type;

FIG. 5 is an example of a spectral comparison between a middle and anedge spectral measurement;

FIG. 6A shows a plot of a diagnostic spectral feature for chert;

FIG. 6B shows a plot of the chert spectral ratio plotted against theweight percent of SiO₂;

FIG. 7A shows a plot of a diagnostic spectral feature for iron; and

FIG. 7B shows a plot of the iron ratio against the weight percent ofiron.

DETAILED DESCRIPTION OF THE EMBODIMENTS

To achieve an automated mine, a fast and objective method of assessingthe geology of exposed mine faces is needed. One method described hereinis the use of spectral data.

Reflectance and emittance spectroscopy techniques can be used to obtaininformation regarding the chemical composition of an object or material.An advantage of spectroscopy is that it can be used at close or farrange.

One object that may be analysed by using spectroscopy is a rock body.While the variations in material composition often cause shifts in theposition and shape of spectral features and the spectral features to beexamined in an ore body can be quite complex, spectroscopy still hasgreat potential to estimate and classify key geological properties suchas rock type and/or ore grade.

Spectral data can be used to distinguish different rock types. Specificabsorption features can be associated with certain minerals and can beused diagnostically to identify the minerals present. Both spectralratios and hyperspectral classification are techniques that can be usedto classify rock type for an automated mine.

Hyperspectral images are produced by imaging spectrometers orhyperspectral cameras. Hyperspectral sensors collect data in hundreds ofbands. These measurements produce a continuous spectrum that, afteradjustments and corrections, can be compared with libraries ofreflectance spectra. Typically, hyperspectral cameras collect allspectra across a spatial line in the image and scanning is required inorder to build up a spectral image. By using an imaging spectrometer orhyperspectral camera in conjunction with a geometry scanner (such as alaser scanner used for Light Detection and ranging (Lidar) scanning) itis possible to build a geological map and model of a scanned terrainsuch as a mine bench face.

Hyperspectral imaging provides narrow spectral bands over a continuousspectral range. On the other hand, multispectral imaging providesseveral images at discrete and narrow bands, and does not produce theentire spectrum reflected by an object. An example of a multispectralsystem is Landsat, or the FluxData FD-1665 multispectral camera.

Multispectral spectrometers are more economical to use thanhyperspectral spectrometers. However, multispectral images do notinclude as much information as hyperspectral images. Nevertheless, ifthe measured wavelength bands correspond to characteristic reflectancebehaviour of the relevant material, wavelength ratios can be extractedfrom the multispectral data and can be used to identify rock types.

Spectral ratios provide quantitative and objective sampleclassification, which may be more accurate and consistent thansubjective visual classification. Spectral data may contain moreinformation than can be observed by visual inspection. Improvedclassification can lead to greater efficiencies in mining as thelocation of ore and waste can be more accurately defined. Also, usingspectral ratios may provide automated mining with an automatedclassification technique that is objective and that can be completed inreal-time. Once rock types have been determined based on spectralratios, the material may be removed from the bench and then transportedfor processing according to the rock type as determined.

1. System Overview

With reference to FIG. 1, there is shown a system 100 in which the rockclassification methods described herein may be implemented.

The system 100 comprises a scanning module 102 which, in this case,includes two spectral cameras 104 arranged to take spectral measurementsof an area of interest. These spectral cameras can be hyperspectralcameras. For example, for examination of iron ore bodies it has beenfound that Neo HySpex VNIR and SWIR cameras having the followingcharacteristics are suitable.

Sensor VNIR 1600 SWIR 320m Spectral range 0.4-1 μm 1.3-2.5 μm Spatialpixels 1600 × 1200 320 × 256 # bands 160 256 Digitisation 12 bit 14 bit

The VNIR camera may be used to detect iron ore whereas clay minerals canbe detected by SWIR images. Different cameras may be used either aloneor in combination depending on the nature of the data desired.

Alternatively, the spectral cameras can be multispectral cameras, suchas the Landsat multispectral scanner (MMS), or the FluxData FD-1665multispectral camera.

The scanning module 102 may also include a geometry scanner 106 (such asa Riegl LMS-Z420i laser scanner) for taking measurements relating togeometrical characteristics of the region of interest. The geometryscanner 106 may include an RGB camera 108 and a range scanner 110.

The scanning module 102 is coupled to a mobile vehicle 112 which may bea self-propelled vehicle or may be a trailer or similar to be towedbehind a prime mover. The vehicle or prime mover may be directlycontrolled by a driver, under remote robotic control, or may be anautonomous (i.e. artificially intelligent) unit. The vehicle 112 carriesa transmitter 114 for transmitting measurement data from the spectralcameras 104 and geometry scanner 106 to a processing station 120. In oneembodiment the measurement data is transmitted using standard radiofrequency protocol.

In an alternative arrangement, the spectral image is obtained scanningdrilled sample material in a laboratory or other offsite setting. Inthis case the spectral data may be uploaded to the processing station120, for example by connecting the cameras directly to the computingsystem 122.

More specifically, an ASD FieldSpec® 3 hyperspectral spectrometer may beused which consists of three calibrated spectrometers that have a totalspectral range of 0.350 to 2.500 μm. The spectra may be taken at aheight of 15 cm using an eight degree field of view. This will result inthe spectrum being an average of the material within a 2 cm diameterarea. Either natural light or artificial light can be used when theimages are taken. A halogen lamp may be used as the light source if anartificial light source is employed. Allowing the lamp to warm up beforethe spectra are recorded reduces the variation in the light source.

Before each spectrum is collected, a spectrum of a white spectralon®plate may be taken under the same conditions. The spectrum is thencalibrated to reflectance using a ratio of the spectrum to thespectralon® reference spectrum. The spectrum is then calibrated toabsolute reflectance using the reflectance characteristics of thereference plate.

The processing station 120 is in the form of a remotely locatedcomputing system 122 including a receiver input 124 which can be wiredor wireless. The computing system 122 is operable to process themeasurement data gathered by both the cameras 104 and geometry scanner106 so as to produce geological survey data.

The data is subsequently processed by the computing system 122 in orderto determine the composition of the material of which a spectral imagewas taken. The computing system 122 may employ standard computerhardware such as a motherboard 126, a central processing unit 128, arandom access memory 130, a hard disk 132, and networking hardware 134.In addition to the hardware, the system 120 includes an operating system(such as the Microsoft Windows™ XP Operating System, which is made byMicrosoft Corporation) that resides on the hard disk 132 and whichco-operates with the hardware to provide an environment in which thesoftware applications can be executed. The processing station 120further includes a visual display unit 136 and a database 138 forstoring the measured data and computed spectral and materialcharacteristics.

In one embodiment, the spectral information could be processedautomatically using a program such as Matlab running on the computingsystem to rapidly produce real-time rock classifications.

In a further arrangement the spectral information may be processed on acomputing system mounted on the vehicle 112.

2. Method Overview

FIGS. 2A and 2B outline two processes that are used in the rock typeclassification as described herein. First, suitable classificationcriteria are determined for an application, and then these criteria areused to perform the rock type classification.

2.1 Determining Classification Criteria

With reference to FIG. 2A, the method 220 for determining classificationcriteria is typically performed offline. The method 220 will beperformed for each area of interest where rock type classification willbe performed.

One particular example discussed below is the identification of oresfrom the West Angelas Mine located in the Eastern Pilbara in northernWestern Australia. The geology includes shales, martite/goethite orezones and banded iron formations (BIF). It will be appreciated that themethodology is also applicable to the identification of minerals inother ore bodies.

At step 222 the relevant minerals that are present in the area ofinterest are identified. For the West Angelas Mine example, theseminerals are the shales, martite/goethite ores and BIF. At step 224 thecharacteristic spectra for each mineral are obtained, for example byscanning samples of these minerals to produce spectral images. FIG. 3 isan example of the spectra 300 of the group of minerals present in theWest Angelas Mine.

At step 226 a spectrum (or group of spectra) is identified that has adiagnostic spectral feature. A diagnostic spectral feature is a spectralfeature that is characteristic for a specific material or group ofmaterials. This is, for example, an absorption feature at a specificwavelength. For the spectra 300 shown in FIG. 3, one such diagnosticspectral feature can be seen in the manganiferous shale spectrum 301that has a characteristic drop in reflectance towards the lowerwavelengths. Relative absorption band depth analysis may be used tocharacterise this spectral feature.

At step 228 two wavelength bands are identified that are associated withthe identified diagnostic spectral feature, and which define thewavelengths used for determining a spectral ratio. For the manganiferousshale spectrum 301, the two wavelengths 302 (or wavelength bands) are1.750 to 1.760 μm and 0.720 to 0.730 μm. The ratio obtained by dividingthe sum of the reflectance for 1.750 to 1.760 μm by the sum of thereflectance for 0.720 to 0.730 μm (the so-called manganiferous shaleratio) can be used to distinguish manganiferous shale from the otherrock types in the group.

At step 230 a diagnostic criterion is determined for use inclassification with the spectral ratio. In one arrangement a thresholdvalue is identified for the spectral ratio. When a spectral ratio isdetermined for all the spectra at the relevant wavelengths, the specificmineral or group of minerals will have a certain, distinctive ratio. Forexample, for the wavelength bands 1.750 to 1.760 μm and 0.720 to 0.730μm, manganiferous shale can clearly be distinguished as can be seen inFIG. 4 where the calculated ratios are plotted against the weightpercent of manganese oxide for the rock types in the group. If a ratiohigher than 2.4 is obtained for a ratio between these two wavelengthbands, then the material can be classified as manganiferous shale, and arock with a ratio lower than 2.4 is not manganiferous shale. A suitablethreshold for the manganiferous shale ratio is therefore 2.4.

At 232 the identified wavelength bands and the ratio threshold for aspecific mineral (or group of minerals) are saved. The process flow thenreturns to step 226 to look iteratively for diagnostic spectral featuresthat may be used to classify other minerals in the group. The spectrafor the rock types characterised by the ratio determined in step 228(for example the manganiferous shales) are removed from the group ofcharacteristic spectra at step 234. This step makes it easier todistinguish between the remaining minerals.

The application of method 220 to the West Angelas example is furtherdiscussed in section 8 below, with reference to FIGS. 6, 7A and 7B. TheWest Angelas rock types may be distinguished using four spectral ratios.

2.2 Rock Type Classification

The spectral ratios determined in method 220 may be used in method 200to provide a rapid diagnostic technique to provide a quantitativeclassification of mine lithologies.

With reference to FIG. 2B, the system 100 shown in FIG. 1 may be used inmethod 200 to determine rock types present in an area of interest.

The cameras 104 are used to record the spectral reflectance in an areaof interest, for example scanning a surface of a rock body to obtainspectral data from the rock body surface. The multiple spectra obtainedfrom successively scanned regions of the rock body surface in spectralmeasurement step 202 may be recorded in the database 138 of theprocessing station 120. For example, n different spectra may bemeasured.

The computing system 122 then inspects the stored spectral data and atstep 204 determines if any of the spectra correspond to high anglemeasurements. There may be, for example, in different spectracorresponding to high angle measurements. These measurements do notprovide an accurate portrayal of the spectral characteristics of thematerial, and therefore at step 206 the remaining spectra are selected,that is the spectra that do not correspond to high-angle measurements.The remaining data is used for identifying rock types present in thescanned regions of the rock body surface.

At step 208 the remaining (i=n−m) spectra are considered, and ratios arecalculated for the wavelength bands associated with diagnostic spectralfeatures of the rock types present in the ore body. The wavelength bandsused are those determined in method 220.

As described above, multiple spectra are obtained at step 202, and highangle measurements are removed from the group of spectra. However, themethod can also be performed on a single measurement. If one spectrum isobtained at step 202, then that spectrum is inspected at step 204 todetermine if it is a high angle spectrum. If it is, then the processends and no classification is done. If it is not, then the processproceeds to step 208.

At step 210 a calculated ratio is compared to the correspondingthreshold value for the specific rock types. These are the thresholdvalues as determined in step 230 in FIG. 2A. If the ratio meets thediagnostic criteria, then the rock type can be classified at step 212.

As shown by arrow 214 this process may be a sequential; iterativeprocess, i.e. after considering the ratio for one rock type (or mineralgroup) and completing the classification for that rock type, then steps208 to 212 may be repeated for a next rock type based on a differentratio and its corresponding classification threshold. However, the orderof the steps can be different, for example a form of batch processingmay be done: all the relevant ratios for a spectral measurement may bedetermined, and then following this step the calculated ratios may besequentially compared to all the relevant threshold values in order toclassify the rock types. The order of classification may be the same asthe order established in the iterative analysis of steps 226, 228, 230and 234.

Appendix 1 shows an example of a Matlab script that may be used toidentify rock types from a mineral spectrum. The script may, forexample, run on computing system 122 or on a computing system on thevehicle 112. The spectrum is input in a file with wavelength in a firstcolumn and reflectance in a second column. The script then extracts thevalues in the six spectral ranges required and sums the values. Thespectral ratios are calculated and are sequentially used to classify thespectrum as manganiferous shale; shale or water reactive clay; martitegoethite; ochreous goethite; or chert rich. The ratio values used forthe classification are discussed with reference to FIGS. 3, 4, 6A, 6B,7A and 7B.

The process may also record the location at which each of the spectralmeasurements was captured.

3. Spectroscopy

Spectroscopy is the study of light that has been emitted, reflected orscattered from a solid, liquid, or gas. When photons enter a mineralthey are either absorbed, reflected from grain surfaces or refractedthrough the mineral. The photons that are reflected or refracted arecalled scattered. Some of these photons are scattered away from thesurface and can be detected. The wavelengths of light which are absorbeddepend on the material. For example in the case of a rock, a scatteredspectrum contains information about abundance of constituent minerals,chemical composition and structure. Photons are also scattered off thesurface by specular effects without interacting with the material. Thesephotons do not contain information about the material and form a lowbackground spectrum that typically has a slight effect on the depth ofthe absorption bands.

The light scattered from the material can be recorded using aspectrometer that records light across a large number of continuousbands. These bands are narrow and, ideally, are the same spectral widthfor the entire range measured. Some systems, such as the LandsatThematic Mapper™ and the MODerate Resolution Imaging Spectroradiometer(MODIS), have only a few broad bands that are widely spaced andtherefore are not considered spectrometers.

Spectroscopy can be used to obtain geological information from samplesin the laboratory, or in natural settings via imaging spectrometers thatare used in the field, mounted on aircraft or on satellites. Advantagesof using spectroscopy include sensitivity to crystalline and amorphousmaterials and its usefulness for both close and distant objects. Nosample preparation is needed and the technique is non-destructive. Someminerals can be identified by diagnostic absorption features. Thereforespectroscopy can be used to obtain information about the mineralcomposition of rocks and identify them from remotely obtained data.Also, a continuous data set can be obtained, allowing large areas to bestudied and compared. However, not all minerals have diagnosticabsorptions and some minerals, for example quartz, only have diagnosticabsorptions outside of the visible near infrared (VNIR) and short waveinfrared regions (SWIR) normally used.

Absorption bands are caused by electronic and vibrational processesinside materials. The electronic processes are due to the absorption ofa photon by an atom or ion, which is shifted to a higher energy state asa result. A lower energy state is then achieved by releasing a photon,usually at a different wavelength. This process can cause heating of thematerial. Electronic absorption bands can also occur when the absorptionof a photon causes an electron to move between ions or into theconduction band where it can move freely through the lattice. Thecrystal structure varies from mineral to mineral and this produces thecharacteristic absorption features. Vibrational absorption occurs when alattice molecule absorbs the energy of a photon and converts it tovibrations. For minerals, electronic processes produce very wide bandsthat are mostly in the ultraviolet or visible regions. Their frequencydecreases through the visible range and there are very few in theinfrared range. Usually electronic transfers are not seen at wavelengthslonger than the iron band at 1 μm. Vibrational processes require lessenergy and produce sharper bands that occur at wavelengths greater than2.5 μm. Therefore, in the SWIR and VNIR, there are no fundamentalvibrational bands but only bands that are harmonic systems. These areovertones or combinations of the fundamental bands in the mid and farinfrared. The intensity and frequency of these bands decrease towardsthe visible range as the probability of the required combinationoccurring decreases. Some minerals, especially halides, display spectralfeatures that cannot be explained by their composition and are insteadcaused by colour centres in imperfect crystals. When these crystals areirradiated their electrons are excited, but instead of returning to thepositively charged holes after the irradiation is removed, they canbecome bound to the defect.

When spectra are taken using natural light the atmospheric absorptionsshould be considered, as these affect the spectra. The main absorptionsin the VNIR and SWIR are caused by water and occur at 1.13, 1.4 and 1.9μm. Other atmospheric absorptions are caused by oxygen at 0.76 μm, andCO₂ at 1.57, 1.61, 2.01, and 2.06 μm. However, when artificial light isused these atmospheric absorptions are not present and these regions ofthe spectrum can be used to obtain information. For example, hydroxylgroups have similar absorptions to water but do not absorb at 1.9 μm.Therefore a spectrum containing only the 1.4 μm absorption indicateshydroxyl, but a spectrum with absorptions at 1.4 and 1.9 is indicativeof water.

When a pixel contains multiple minerals the spectrum produced is amixture of the spectral features of all the minerals in the pixel. Themixing can be either linear or non-linear depending on the size of theminerals. Linear mixing occurs when each photon only interacts with onemineral type. The spectrum produced is a sum of the spectra produced byeach type. The amount of influence each mineral type has on the spectrumis proportional to the area it covers within the pixel. Non-linearmixing occurs when each photon can interact with multiple mineral typesand produces a more complex spectrum that is not a linear combination ofthe mineral spectra. Minerals generally create a non-linear mixture dueto the small grain size.

Spectroscopy is sensitive to changes in the crystal structure orchemistry of a material, which can cause very complex spectra in naturalmaterials such as rocks. Mixtures generally do not have a linear effecton the spectra. As the photons contact multiple minerals there is a highprobability that, when there is a mixture of light and dark grains, aphoton will interact with a dark grain. As the dark grain will have ahigher absorption a small percentage of dark grains can reduce thereflectance of the spectra by much more than their weight percent. Thegrain size also affects the amount of light scattered and absorbed as itdepends on the volume to surface ratio. A small grain has more surfacearea to reflect from and a shorter internal path, which reducesabsorption. The spectra of a material can also be affected by a mineralwhich does not have absorption features in the observed wavelengths. Forexample, quartz does not have absorption features in the VNIR or SWIRwavelengths but increases the overall reflectance of the spectrum.Impurities from trace elements are commonly found in minerals and thesecan have a large effect on the spectra of the mineral. This isespecially noticeable in the visible wavelengths.

The data from a spectrometer is controlled by its spectral range,spectral bandwidth and spectral sampling. The spectral range needs tocover enough diagnostic features to be capable of identifying thematerials present. Two ranges used in remote sensing are the visiblenear infrared (VNIR), covering 0.4 to 1.0 μm, and the short waveinfrared (SWIR), covering 1.0 to 2.5 μm. The spectral bandwidth is thewidth of each individual spectral band in the spectrometer. A narrowerspectral bandwidth allows for narrower absorption features to bedetected, if there are sufficient adjacent spectral samples. Bandwidthsgreater than 25 nm lose the ability to resolve important mineralabsorption features. More fine detail will be observed with narrowerbandwidths, for example 5 μm. The shape of the bandpass for thespectrometer band is commonly Gaussian. The width of the bandpass isdefined as the width, in wavelength, at half the maximum response of thefunction, called the full width half maximum (FWHM). Spectral samplingis the distance, in wavelength, between the spectral bandpass profilesfor each spectrometer band. Spectral sampling can be combined with thespectral bandwidth to give resolution. The spectral resolution islimited by the amount of light available. As the bands become narrowerthe amount of light received by each channel is reduced, generallyincreasing the signal to noise ratio (S/N ratio). The S/N ratio must belarge enough that the spectral features studied can be distinguishedfrom the background. The ratio required therefore depends on thestrength of the spectral feature. The ratio also depends on the detectorsensitivity, the spectral bandwidth and the light intensity beingmeasured.

5. Spectral Ratio Comparisons

Mine rock type classifications are based on chemical and mineralogicalcriteria, but during operation subjective visual classifications areoften used. In an automated mine, this is not possible and anothertechnique must be employed. Spectral ratios provide a rapid diagnostictechnique to provide a quantitative classification of mine lithologies.Spectral ratios require only multispectral systems, not hyperspectralsystems. This reduces the amount of data produced and therefore theamount of processing and data transmission required.

Hyperspectral data contains the most information about the materialscanned. However, while the extra data is beneficial when undertakingresearch, the entire data set is not necessarily required in workingmine conditions, where fast and easily acquired data is better.Therefore a multispectral system using spectral ratios may have anadvantage over a hyperspectral system.

Hyperspectral files are large, especially when scanning bigger areas.This makes the processing slow. Additionally, in an automated mine, alldata would be sent from the spectral system to the operations centre.The transfer of very large amounts of data could cause difficulties inthis process. Spectral ratios only require certain bands to be scannedand therefore produce a much smaller data set. The ratios could beeasily and quickly processed on site by a computer connected to thespectral system. The final classification would then be the onlyinformation sent to the operations centre.

The characteristic spectral features of the different minerals can beused to identify the rock type. One method of comparing two spectra isrelative absorption band depth analysis. This involves taking a ratiousing a point in the spectrum which corresponds to an absorption featureof interest. Several bands near the shoulder of an absorption band aresummed and divided by the sum of several bands near the minimum of theabsorption feature. This gives a relative absorption depth which can beused to detect diagnostic mineral absorption features with lessinterference from the background reflectance.

In the ore body considered, manganiferous shale has a spectrum which isdistinctly different from the other rocks. Referring to FIG. 3,manganiferous shale 301 has a steady -decrease in reflectance towardsthe lower wavelengths. This spectral feature can be described using aspectral ratio between the wavelength bands 302, namely 1.750 to 1.760μm and 0.720 to 0.730 μm. The ratio obtained by dividing the sum of thereflectance for 1.750 to 1.760 μm by the sum of the reflectance for0.720 to 0.730 μm (manganiferous shale ratio) can be used to distinguishmanganiferous shale from other rock types. Referring to FIG. 4, whenthis ratio 304 is plotted against the weight percent of manganese oxide,it can be observed that the manganiferous shale 306 is distinct from theother rock types. Therefore, if a rock has a ratio higher than 2.4 itcan be confidently classified as manganiferous shale, and a rock with aratio lower than 2.4 is not manganiferous shale.

When an absorption feature is particularly well defined, such as thehalloysite features, they can be used to predict the concentration orweight percent of a compound in the rock. Spectral ratios can thereforebe used to distinguish different rock types.

The spectral ratios determined for the different rock types can be usedto identify rock types from a mineral spectrum. The manganiferous shaleratio (1.750 to 1.760 μm/0.720 to 0.730 μm), the shale ratio (2.220 to2.230 μm/2.200 to 2.210 μm) and the 1.727 μm/1.016 μm ratio may be used.To reduce the effect random error has on the ratio, the 1.727 μm/1.016μm ratio can be expanded to be the sum of the reflectance for 1.720 to1.730 μm divided by the sum of the reflectance for 1.010 to 1.020 μm.

With reference to FIG. 2B, the reflectance at each wavelength of thespectra remaining after step 206 is evaluated by the processing system120. Spectral ratios are calculated according to the positions of thecharacteristic (or “strong”) spectral features as described above. Theseratios are then used to classify the spectrum as manganiferous shale;shale or water reactive clay; martite goethite; ochreous goethite; orchert rich. The ratio values used for each of these are those listedabove.

When a spectrum is taken under natural light, some wavelengths areabsorbed by the atmosphere. This results in the spectrum containing nodata at certain wavelengths. For example, the absorbed wavelengthsoverlap the clay absorption features at 1.4 μm. Therefore, when choosingwavelengths for spectral ratios it is useful to choose wavelengths whichare not affected by the atmospheric absorptions.

In mine conditions atmospheric absorptions must be considered becausescanning will probably not be done using artificial light. The mainabsorptions are those from water at 1.4 and 1.9 μm. Other atmosphericabsorptions are due to oxygen at 0.76 μm, and CO₂ at 1.57, 1.61, 2.01,and 2.06 μm. The spectral ratios used to identify material shouldtherefore be chosen so that they do not overlap these absorptionfeatures. If this is done, then atmospheric absorptions will not affectthe spectral ratio classification.

Many rock types can be effectively classified using spectra taken withnatural light. This means that the method of using spectral ratios asdescribed herein can be used in an outdoor setting such as a mine.

6. Edge Effects

The orientation of a scanned surface to both the light emitting sourceand the receiving spectrometer has an effect on the spectrum obtained.The amount of direct and indirect light on a scanned drill core alsoinfluences the spectrum produced. The centre of the core has the mostdirect light and the component of indirect light increases towards theedge. These effects cause difficulties in classifying the edges of thecores when using the spectral angle mapper.

When spectra are taken at a high angle, absorption features occur wherethe spectrum has a lower reflectance at certain wavelengths. Theseabsorptions cause the spectra to be incorrectly classified. Data takenat a high angle can be identified by a ratio such as the edge anomalyratio (1.520 to 1.540 μm/0.700 to 0.720 μm). This data can then bediscarded to prevent incorrect classifications.

To illustrate the extent of the effect that the angle has on thespectrum scanned, three regions on Specim core scans can be considered.The areas that the cores were from appeared visually homogenous inochreous goethite, martite goethite and water reactive clay. For eachregion areas of approximately 100 pixels were chosen, one in the centreof the core, one near the edge of the core and one in the middle of theother two spectra. The averages of these areas were taken and theresulting spectra compared. The middle and edge spectra were eachdivided by the centre spectrum and the log was taken of the resultingratio. This value shows the difference between the spectra, where aresult of zero indicates that the spectra are identical.

Referring to FIG. 5, the ochreous goethite spectra show the expectedoverall decrease in reflectance away from the centre. However, there isalso a change in some of the spectral features. This is seen clearly inthe spectral comparison 500 in FIG. 5. The middle spectrum has a lowerreflectance which is mostly steady for both the VNIR and SWIR regions.The spectral jump between the two spectral systems can be observed atapproximately 1 μm. At the wavelengths near the ends of both spectralsystems there is an increase in the noise which has a relatively largeeffect on the spectral comparison, but has no spectral significance. Theedge spectrum is very similar to the middle spectrum in the VNIR, buthas a very different SWIR spectrum. There are three absorptions around1.2, 1.4 and 1.7 μm and an overall decrease towards longer wavelengths.The absorptions are not true absorptions but represent areas where theedge spectrum has a lower reflectance than the centre spectrum. Thesefeatures have a large effect on the spectral angle mapper classificationas it changes the overall shape of the spectrum.

The absorption features identified in the edges of the cores may be usedto distinguish data from high angles on both cores and pit walls. Thisdata could then be discarded, reducing the amount of incorrectlyclassified rock. The spectral ratio obtained by dividing the sum of thereflectance for 1.520 to 1.540 μm by the sum of the reflectance for0.700 to 0.720 μm (edge anomaly ratio) is one possibility. When thisratio is calculated for the spectra used above it is observed that thecentre spectra have a ratio close to zero and the ratio increasestowards the edge. Spectra with a ratio greater than 1.1 could beconsidered to be from a high angle, and discarded. This ratio may, forexample be used in step 204 of method 200.

7. West Angelas Example

The system and method described above was used on rock samples from theWest Angelas mine in the Eastern Pilbara in northern Western Australia.

7.1 Multispectral Images and Spectral Ratios

Method 220 was applied to the West Angelas ore body as follows. Theimportant lithological end members for the West Angelas Mine werevisually identified and representative areas of core were chosen (step222). For step 224, two individual spectra from each of these areas weretaken using an ASD Field Spec® 3 spectrometer, which consists of threecalibrated spectrometers that have a total spectral range of 0.350 to2.500 μm. The spectra were taken at a height of 15 cm using an eightdegree field of view. This resulted in the spectrum being an average ofthe material within a 2 cm diameter area. A halogen lamp was used as thelight source. The lamp was allowed to warm up before the spectra wererecorded to reduce the variation in the light source.

Before each end member spectrum was collected, a spectrum of a whitespectralon® plate was taken under the same conditions. The end memberspectrum was then calibrated to reflectance using a ratio of the endmember spectrum to the spectralon® reference spectrum. The spectrum wasthen calibrated to absolute reflectance using the reflectancecharacteristics of the reference plate.

Manganiferous shale has a spectrum which is distinctly different fromother rocks. Referring to FIG. 3, the ratio obtained by dividing the sumof the reflectance for 1.750 to 1.760 μm by the sum of the reflectancefor 0.720 to 0.730 μm (manganiferous shale ratio) can be used todistinguish manganiferous shale 301 from other rock types. Referring toFIG. 4, when this ratio 304 is plotted against the weight percent ofmanganese oxide, it can be observed that the manganiferous shale 306 isdistinct from the other rock types. Therefore, if a rock has a ratiohigher than 2.4 it can be confidently classified as manganiferous shale,and a rock with a ratio lower than 2.4 is not manganiferous shale.

The samples that contain clay show significant spectral absorptionswhich are not present in the other samples. These absorptions can beused to distinguish the clay and shale samples from the other rocktypes. The ratio obtained by dividing the sum of the reflectance for2.220 to 2.230 μm by the sum of the reflectance for 2.200 to 2.210 μm(shale ratio) is greater than 1.04 for the shales and clays due to thehalloysite absorption feature around 2.2 μm. The other rock types have aratio less than 1.03 as without the halloysite absorption the spectrumis a similar height or slightly higher at 2.200 to 2.210 μm than 2.220to 2.230 μm. The depth of the absorption is related to the amount ofhalloysite in the sample, but it is also affected in the manganiferousand West Angelas shales by the change in the overall spectral shape whenthere is manganese present. The ratio can be used to separate shales andclays from the other rock types as a ratio higher than 1.4 indicates thepresence of clay. If the manganiferous shale was separated first usingthe manganiferous shale ratio, the distinction between the rock typeswould be greater. If the clay present was not halloysite the ratio wouldneed to be shifted slightly to match the shift of the absorption featureas it occurs at a slightly different wavelength for each type of clay.Although clay also has a distinct absorption feature around 1.4 μm, thisabsorption was not used as it overlaps with a water absorption featureand therefore could not be used with natural light.

Once the shale and clay samples have been identified using the shaleratio the remaining samples are easier to separate. Although silicon hasno absorption features in the range used, chert and goethitic BIF can bedistinguished from ochreous goethite and martite goethite by the depthof the iron absorption, the overall shape and the maximum reflectance(see the spectra 604 in FIG. 6A). When the ratio obtained by dividingthe sum of the reflectance for 1.310 to 1.320 μm by the sum of thereflectance for 0.740 to 0.750 μm (chert ratio) is plotted against theweight percent of silicon dioxide as shown in FIG. 6B, these samples aredivided into separate populations with the chert 602 clearlydistinguishable. The martite goethite samples give a ratio below one;the chert and goethitic BIF have ratios between 1.2 and 1.6; and theochreous goethite samples have ratios greater than 1.6. There is only asmall gap between the goethitic BIF and the ochreous goethite because ofthe large effect even a small amount of goethite can have on thespectrum. A similar division can be observed when the maximumreflectance for the spectra is plotted against the weight percent ofsilicon dioxide. However, this method has an even smaller gap betweenthe goethitic BIF (with a maximum of 0.65) and the ochreous goethite(with a minimum of 0.66). A larger sample set could be used to confirmthe boundary between the goethitic BIF and ochreous goethite.

The 1.727 μm/1.016 μm ratio has been used to detect alteration in thesilicification of jasperoids. When this ratio is plotted against theweight percent of silicon dioxide for the ore, BIF and chert samples itclearly distinguishes the goethitic BIF (maximum 1.62) from the ochreousgoethite (minimum 1.82). The martite goethite samples give a ratio below1.1; the chert and goethitic BIF have ratios between 1.3 and 1.7; andthe ochreous goethite samples have ratios greater than 1.8.

When the shale and chert ratios are used to remove the other sampletypes, the iron ores can be distinguished using the spectral differencesin the iron features as shown in the spectra 700 plotted in FIG. 7A.When the ratios 702 obtained by dividing the sum of the reflectance for1.210 to 1.220 μm by the sum of the reflectance for 0.735 to 0.745 μm(iron ratio) is plotted against the weight percent of iron, the ironores are clearly divided into two groups. Samples with a ratio below 1.1are martite goethitic 704 and contain above 63 weight percent iron.Samples with a ratio above 1.4 are ochreous goethite 706 and containless than 60 weight percent iron. This ratio can therefore be used toseparate the iron ores and distinguish their grade. When the iron ratiois plotted against the weight percent of silicon dioxide similar groupscan be observed due to the silicon content having an inverse correlationto the iron content. Samples with a ratio below 1.1 contain less than1.5 weight percent silicon dioxide. Samples with a ratio above 1.4contain between 2.5 and 3.0 weight percent silicon dioxide.

When an absorption feature is particularly well defined, such as thehalloysite features, they can be used to predict the concentration orweight percent of a compound in the rock. For the West Angelas rocks,the only mineral with a spectral ratio sufficiently unaffected by theother minerals present is halloysite. The ratio obtained by dividing thesum of the reflectance for 2.175 to 2.180 μm by the sum of thereflectance for 2.205 to 2.210 μm (Al₂O₃ ratio) can be used to predictthe weight percent of Al₂O₃ in the sample. When a regression analysis isperformed using the weight percent Al₂O₃ as the independent variable xand the Al₂O₃ ratio as the dependant variable y it gives the formula:

y=(0.0059±0.0005)x+(1.014±0.007)

This has an adjusted R squared value of 0.855 and a standard error of0.028. The above equation can then be used to estimate the weightpercent Al₂O₃ from a spectrum for which there is no information aboutthe rock composition.

Spectral ratios can therefore be used to distinguish different rocktypes from the West Angelas mine. The manganiferous shale and shaleratios can be used to separate the different shales and clays from theother rock types. The chert ratio and the maximum reflectance can thenbe used to separate the goethitic BIF and chert from the iron ores.However, the spectral difference between the rock types is smaller heredue to the strong influence of iron in the spectra and the lack ofdistinguishing features for silica.

Spectral ratios use the comparative depth of absorption features andchanges in the overall shape of the spectrum to identify rocks usingonly a small portion of the hyperspectral range. The West Angelas minerock types have been distinguished using four ratios: the manganiferousshale ratio (1.750 to 1.760 μm/0.720 to 0.730 μm); shale ratio (2.220 to2.230 μm/2.200 to 2.210 μm); 1.727 μm/1.016 μm ratio (1.720 to 1.730μm/1.010 to 1.020 μm); and martite goethite ratio (0.920 to 0.930μm/0.850 to 0.860 μm).

The four ratios and their corresponding classification thresholds may beused in method 200 to determine rock types based on spectra measured atthe West Angeles mine. This may provide real-time automated geologicalassessment that may be used in removing material from a mine bench faceand in transporting removed material for processing in accordance withthe rock-type determination.

7.2 Discussion of Example Results

Spectral data can be used to identify the main minerals present, andtherefore objectively classify a mine rock sample.

The rock types of the West Angeles mine could be classified using fiveratios, one ratio to identify high angle spectra and four ratios todetermine rock type. The data could be acquired using a ten-bandmultispectral system, thereby eliminating the need for a fullhyperspectral system.

Four spectral ratios can be used to quickly and objectively classify theWest Angeles mine rock types. The manganiferous shale ratio (1.750 to1.760 μm/0.720 to 0.730 μm), shale ratio (2.220 to 2.230 μm/2.200 to2.210 μm), 1.727 μm/1.016 μm ratio (1.720 to 1.730 μm/1.010 to 1.020 μm)and martite goethite ratio (0.920 to 0.930 μm/0.850 to 0.860 μm). Thesespectral ratios were chosen so that they are not affected by theatmospheric absorptions that occur when natural light is used.

Non-linear mixing effects were observed in the spectra due to the smallgrain size in the samples. This results in even a small amount ofgoethite in a chert or BIF sample having a large effect on the spectrumobserved. Therefore the most difficult rock types to distinguish are theochreous goethite, goethitic BIF and chert.

Spectral ratios provide a fast and objective method of classifying theshales, martite/goethite ore zones and BIF present at the West Angelesiron mine. The ratios may be used to provide real-time automatedgeological assessment. Currently a subjective visual classification isused that can lead to incorrect classification and which cannot beautomated. Therefore, an objective classification such as the spectralratio techniques described herein may be fundamental to the developmentof future automated mining techniques.

It will be understood that the invention disclosed and defined in thisspecification extends to all alternative combinations of two or more ofthe individual features mentioned or evident from the text or drawings.All of these different combinations constitute various alternativeaspects of the invention.

The present application claims priority from Australian provisionalapplication AU2010900467, “Determination of rock types by spectralscanning” filed 5 Feb. 2010, the entire contents of which areincorporated herein by reference.

APPENDIX 1 Matlab script for distinguishing West Angelas mine rock types%clear former values clear %load spectrum (without headings) loadspectra.txt %create strings for the wavelength and reflectance datawavelength=spectra(:,1); reflectance=spectra(:,2); %manganiferous shaleratio %get the reflectance values for 1750-1760 nm fori=1:length(wavelength)   if wavelength(i,1)>=1750;    mshale1(i,1)=reflectance(i,1);   else mshale1 (i,1)=NaN;   end endfor i=1:length(wavelength)   if wavelength(i,1)>1760;    mshale1(i,1)=NaN;   end end mshale1_filt=mshale1(~isnan(mshale1));mshale1_total=sum(mshale1_filt); %get the reflectance values for 720-730nm for i=1:length(wavelength)   if wavelength(i,1)>=720;    mshale2(i,1)=reflectance(i,1);   else mshale2 (i,1)=NaN;   end endfor i=1:length(wavelength)   if wavelength(i,1)>730;    mshale2(i,1)=NaN;   end end mshale2_filt=mshale2(~isnan(mshale2));mshale2_total=sum(mshale2_filt); %calculate the ratio 1750-1760nm /720-730 nm Mshale = mshale1_total/mshale2_total; %shale ratio %get thereflectance values for 2220-2230 nm for i=1:length(wavelength)   ifwavelength(i,1)>=2220;     shale1(i,1)=reflectance(i,1);   else shale1(i,1)=NaN;   end end for i=1:length(wavelength)   ifwavelength(i,1)>2230;     shale1(i,1)=NaN;   end endshale1_filt=shale1(~isnan(shale1)); shale1_total=sum(shale1_filt); %getthe reflectance values for 2200-2210 nm for i=1:length(wavelength)   ifwavelength(i,1)>=2200;     shale2(i,1)=reflectance(i,1);   else shale 2(i,1)=NaN;   end end for i=1:length(wavelength)   ifwavelength(i,1)>2210;     shale2(i,1)=NaN;   end endshale2_filt=shale2(~isnan(shale2)); shale2_total=sum(shale2_filt);%calculate the ratio 2220-2230 nm / 2200-2210 nm Shale =shale1_total/shale2_total; %chert ratio %get the reflectance values for1720-1730 nm for i=1:length(wavelength)   if wavelength(i,1)>=1720;    chert1(i,1)=reflectance(i,1);   else chert1 (i,1)=NaN;   end end fori=1:length(wavelength)   if wavelength(i,1)>1730;     chert1(i,1)=NaN;  end end chert1_filt=chert1(~isnan(chert1));chert1_total=sum(chert1_filt); %get the reflectance values for 1010-1020nm for i=1:length(wavelength)   if wavelength(i,1)>=1010;    chert2(i,1)=reflectance(i,1);   else chert2 (i,1)= NaN;   end endfor i=1:length(wavelength)   if wavelength(i,1)>1020;    chert2(i,1)=NaN;   end end chert2_filt=chert2(~isnan(chert2));chert2_total=sum(chert2_filt); %calculate the ratio 1720-1730 nm /1010-1020 nm Chert = chert1_total/chert2_total; %martite goethite ratio%get the reflectance values for 920-930 nm for i=1:length(wavelength)  if wavelength(i,1)>=920;     martite1(i,1)=reflectance(i,1);   elsemartite1 (i,1)=NaN;   end end for i=1;length(wavelength)   ifwavelength(i,1)>930;     martite1(i,1)=NaN;   end endmartite1_filt=martite1(~isnan(martite1));martite1_total=sum(martite1_filt); %get the reflectance values for850-860 nm for i=length(wavelength)   if wavelength(i,1)>=850;    martite2(i,1)=reflectance(i,1);   else martite2 (i,1)=NaN;   end endfor i=1:length(wavelength)   if wavelength(i,1)>860;    martite2(i,1)=NaN;   end endmartite2_filt=martite2(~isnan(martite2));martite2_total=sum(martite2_filt); %calculate the martite goethite ratio920-930 nm / 850=860 nm Martite = martite1_total/martite2_total;%compare the spectral ratios to the ranges to determine the rock type ifMshale>2.4   Rock = ‘Manganiferous shale’; else   if Shale>1.03     Rock= ‘Shale or Water Reactive Clay’;   else     if Chert<1.1       ifMartite<0.96         Rock = ‘Chert’;       else Rock = ‘Martitegoethite’:       end     else       if Chert<1.75         Rock = ‘Chertrich’;       else Rock = ‘Ochreous goethite’;       end     end   endend

1.-20. (canceled)
 21. A mining system for removing material from a rockbody comprising: a camera system comprising a spectral sensor thatgenerates spectral data at a plurality of discrete and narrow bands whenscanning a surface region of the rock body; a memory to store thespectral data generated by the spectral sensor of the camera system; aprocessing system to access the spectral data in the memory, wherein theprocessor is configured to produce rock type classifying data for therock body by: determining a first spectral ratio between two wavelengthbands of the spectral data, assessing from the first spectral ratiowhether the measurement indicates an absorption feature predetermined tobe derived from angle of reflectance, if the measurement is not derivedfrom angle of reflectance, determine a further spectral ratio betweentwo further wavelength bands of the spectral data, comparing the furtherspectral ratio with a corresponding diagnostic criterion to assesswhether the surface region comprises a first rock type associated withthe further spectral ratio and diagnostic criterion, and classifying thefirst rock type present at the rock body surface based on the assessmentof the further spectral ratio and diagnostic criterion and generatingrock type classifying data for the rock body based on the classifying;and mining equipment configured to: receive the rock type classifyingdata from the processing system, and remove material from the rock bodyaccording to the rock type classifying data.
 22. A method of configuringa mining system for removing material from a rock body, comprising:producing rock type identifications for the rock body in real-time asthe rock body is being scanned by: scanning, by a camera system, asurface of the rock body with a spectral sensor of the camera system,wherein the scanning obtains spectral data from the rock body surface,said spectral data comprising multiple spectra obtained from scannedregions of the rock body surface, determining, by a processing systemcoupled with the camera system, spectral ratios within the multiplespectra, the spectral ratios being derived from a comparison of thespectral data at different specific wavelength bands, distinguishing, bythe processing system, spectra within the multiple spectra that haveabsorption features predetermined to be derived from angle ofreflectance, and identifying, by the processing system, one or more rocktypes present at said regions of the rock body surface based on one ormore spectral ratios determined not to be derived from angle ofreflectance and generating rock type identifying data for the rock bodybased on the identifying; and removing, by the mining equipment,material from the rock body, wherein the mining equipment is configuredto remove the material according to the rock type identificationsgenerated by the processing system.
 23. The method of claim 22, whereinthe camera system is coupled with a mobile vehicle, the mobile vehiclecomprising a transmitter for transmitting measurement data produced bythe camera system to a receiver of the processing system, and whereinthe processing system configures the mining equipment for removal of thematerial from the rock body in real-time based on the rock typeidentifications.
 24. The method of claim 22, wherein a mobile vehiclecomprises the camera system, the processing system, and the miningequipment, and wherein the mobile vehicle is autonomously controlled forremoval of the material from the rock body in real-time based on therock type identifications
 25. The method as claimed in claim 22, whereinthe spectral ratios indicative of rock types are determined fromspectral data at wavelength bands which are not affected by atmosphericabsorptions under natural light illumination.
 26. The method as claimedin claim 22, wherein the rock body is a mineable rock body in a mine andthe method is used to make ore grade assessments of the rock body formining.
 27. The method as claimed in claim 22, wherein the spectralsensor of the camera system comprises a non-hyperspectral multispectralsensor, and wherein spectra scanned by the non-hyperspectralmultispectral sensor include spectra in the Visible Near Infrared Range(VNIR).
 28. The method as claimed in claim 22, wherein the spectralsensor of the camera system comprises a non-hyperspectral multispectralsensor, and wherein spectra scanned by the non-hyperspectralmultispectral sensor include spectra in the Short Wave Infrared Range(SWIR).
 29. A method of controlling mining equipment removal of materialfrom a rock body, comprising: producing, by a processing system, rocktype classifying data for the rock body by: scanning a surface of a rockbody with a spectral sensor to obtain spectral data from the rock bodysurface, said data comprising multiple spectra obtained from scannedregions of the rock body surface, determining, by the processing system,for differing spectra within the multiple spectra a plurality ofspectral ratios as indicators of differing rock types, the spectralratios being derived from a comparison of the spectral data at differentspecific wavelength bands associated with diagnostic absorption featuresof the differing rock types, said comparison comprising dividing a sumof reflectance in a first wavelength band by a sum of reflectance in asecond wavelength band, distinguishing, by the processing system,spectra within the multiple spectra that have absorption featurespredetermined to be derived from angle of reflectance, and classifying,by the processing system, one or more rock types present at the rockbody surface based on one or more spectral ratios determined not to bederived from angle of reflectance and generating rock type classifyingdata for the rock body based on the classifying; and removing, by themining equipment, material from the rock body according to the rock typeclassifying data.
 30. The method as claimed in claim 29, wherein thespectral ratios are determined from spectral data at wavelength bandswhich are not affected by atmospheric absorption under natural lightillumination.
 31. The method as claimed in claim 29, wherein the rockbody is a mineable rock body in a mine and the method is used to makeore grade assessments of the rock body for mining.
 32. A method ofmining, comprising: producing, by a processing system, rock typeidentifying data for a rock body by: scanning a mine bench face with aspectral sensor to obtain spectral data from the rock body at the benchface, said data comprising multiple spectra obtained from scannedregions of the rock body surface, determining, by the processing system,spectral ratios within the multiple spectra, the spectral ratios beingderived from a comparison of the spectral data at different specificwavelength bands, distinguishing, by the processing system, spectrawithin the multiple spectra that have absorption features predeterminedto be derived from angle of reflectance, identifying, by the processingsystem, whether a first rock type is present at regions of the rock bodysurface based on spectral ratios determined not to be derived from angleof reflectance and generating rock type identifying data for the rockbody based on the identifying, and when the first rock type is notidentified as present at the first region of the rock body surface,identifying by the processing system, whether one or more other rocktypes are present at the first region of the rock body surface based onone or more different spectral ratios and generating rock typeidentifying data for the rock body based on the identifying; removing,by a first mining equipment communicably coupled with the processingsystem, material from the bench; and transporting, by a second miningequipment communicably coupled with one or more of the first miningequipment and the second mining equipment, removed material forprocessing in accordance with the rock type identifying data.
 33. Themethod of claim 32, wherein an autonomous mobile vehicle comprises theprocessing system and a camera system, the camera system comprising thespectral sensor, and wherein the autonomous mobile vehicle iscommunicatively coupled with one or more of the first mining equipmentand the second mining equipment.
 34. The method of claim 32, wherein thefirst mining equipment and the second mining equipment comprise the samemining system.
 35. A method of mining comprising: producing rock typeclassifying data for a rock body by: scanning a mine bench face with aspectral sensor to obtain spectral data from the rock body at the benchface, said data comprising multiple spectra obtained from scannedregions of the rock body surface, determining, by a processing system,for differing spectra within the multiple spectra a plurality ofspectral ratios as indicators of differing rock types, the spectralratios being derived from a comparison of the spectral data at differentspecific wavelength bands associated with diagnostic absorption featuresof the differing rock types, said comparison comprising dividing a sumof reflectance in a first wavelength band by a sum of reflectance in asecond wavelength band, distinguishing, by the processing system,spectra within the multiple spectra that have absorption featurespredetermined to be derived from angle of reflectance, classifying, bythe processing system, one or more rock types present at the rock bodysurface based on one or more spectral ratios determined not to bederived from angle of reflectance and generating rock type classifyingdata for the rock body based on the identifying; removing material fromthe bench; and transporting removed material for processing inaccordance with the rock type classifying data.
 36. The method asclaimed in claim 35, wherein the spectral ratios are determined fromspectral data at wavelength bands which are not affected by atmosphericabsorption under natural light illumination.
 37. A method for removingmaterial from a rock body comprising: producing rock type classifyingdata for the rock body by: obtaining, at a processor for processingimages, spectral data from a spectral measurement of a surface region ofthe rock body, determining, by the processor, a first spectral ratiobetween two wavelength bands of the spectral data, assessing, by theprocessor, from the first spectral ratio whether the measurementindicates an absorption feature predetermined to be derived from angleof reflectance, if the measurement is not derived from angle ofreflectance, determining, by the processor, a further spectral ratiobetween two further wavelength bands of the spectral data, comparing, bythe processor, the further spectral ratio with a correspondingdiagnostic criterion to assess whether the surface region comprises afirst rock type associated with the further spectral ratio anddiagnostic criterion, and classifying, by the processor, the first rocktype present at the rock body surface based on the assessment of thefurther spectral ratio and diagnostic criterion and generating rock typeclassifying data for the rock body based on the classifying; removingmaterial from the rock body according to the rock type classifying data;and wherein the spectral measurement comprises images at discrete andnarrow bands, including the wavelength bands for the first spectralratio and the further spectral ratio.
 38. The method of claim 37comprising: determining a plurality of further spectral ratios betweenrespective wavelength bands of the spectral data; and sequentiallycomparing the plurality of further spectral ratios with correspondingdiagnostic criteria to classify a rock type of the surface region. 39.The method of claim 37 wherein the measurement is assessed to be derivedfrom angle of reflectance if the first spectral ratio exceeds an edgeanomaly threshold.
 40. A non-transitory medium containing recordedmachine-readable instructions for controlling the operation of a dataprocessing apparatus by performing one or more operations comprising:producing rock type classifying data for the rock body by: obtaining, ata processor for processing images, spectral data from a spectralmeasurement of a surface region of the rock body, determining, by theprocessor, a first spectral ratio between two wavelength bands of thespectral data, assessing, by the processor, from the first spectralratio whether the measurement indicates an absorption featurepredetermined to be derived from angle of reflectance, if themeasurement is not derived from angle of reflectance, determining, bythe processor, a further spectral ratio between two further wavelengthbands of the spectral data, comparing, by the processor, the furtherspectral ratio with a corresponding diagnostic criterion to assesswhether the surface region comprises a first rock type associated withthe further spectral ratio and diagnostic criterion, and classifying, bythe processor, the first rock type present at the rock body surfacebased on the assessment of the further spectral ratio and diagnosticcriterion and generating rock type classifying data for the rock bodybased on the classifying; and removing material from the rock bodyaccording to the rock type classifying data, wherein the spectralmeasurement comprises images at discrete and narrow bands, including thewavelength bands for the first spectral ratio and the further spectralratio.